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Despite technological advancements to enhance patient health, the risks of not discovering the correct interactions and trends in digital health are high. Hence, a careful policy is required for health coverage tailored to needs and capacity. Therefore, this study aims to explore the adoption of a cognitive computing decision support system (CCDSS) in the assessment of health-care policymaking and validates it by extending the unified theory of acceptance and use of technology model.

A survey was conducted to collect data from different stakeholders, referred to as the 4Ps, namely, patients, providers, payors and policymakers. Structural equation modelling and one-way ANOVA were used to analyse the data.

The result reveals that the behavioural insight of policymakers towards the assessment of health-care policymaking is based on automatic and reflective systems. Investments in CCDSS for policymaking assessment have the potential to produce rational outcomes. CCDSS, built with quality procedures, can validate whether breastfeeding-supporting policies are mother-friendly.

Health-care policies are used by lawmakers to safeguard and improve public health, but it has always been a challenge. With the adoption of CCDSS, the overall goal of health-care policymaking can achieve better quality standards and improve the design of policymaking.

This study drew attention to how CCDSS as a technology enabler can drive health-care policymaking assessment for each stage and how the technology enabler can help the 4Ps of health-care gain insight into the benefits and potential value of CCDSS by demonstrating the breastfeeding supporting policy.

As public–private partnerships (PPPs) have become preferred and veritable approach to deliver affordable housing, the seemingly lack of understanding of the significant factors that impact on success has become a notable setback. This study aims to delineate significant factors that can support decisions in affordable PPP public housing delivery.

Largely, a questionnaire survey was adopted to elicit insights from practitioners, policymakers and experts to develop an evaluative decision support model using an analytical hierarchy process and multi-attribute utility technique approach. Further, an expert illustration was conducted to evaluate and validate the results on the housing typologies.

The results revealed that energy efficiency and low-cost green building materials scored the highest weighting of all the criteria. Furthermore, multi-storey self-contained flats were found to be the most preferred housing typology and were significantly influenced by these factors. From the model evaluation, the scores on the factors of sustainability, affordability, cultural values and accountability were consistent across all typologies of housing whereas that of benchmarking, governance and transparency were varied.

The decision support factors captured varied dimensions of key factors that impact on affordable PPP housing that have not been considered in an integrated manner. These findings offer objective and systematic support to decision-making in affordable PPP housing delivery.

Fish quarantine is a measure to prevent the entry and spread of quarantine fish pests and diseases abroad and from one area to another within Indonesia's territory. Based on these backgrounds, this study aims to identify the knowledge, knowledge management (KM) processes and knowledge management system (KMS) priority needs for quarantine fish and other fishery products measures (QMFFP) and then develop a classification model and web-based decision support system (DSS) for QMFFP decisions.

This research methodology uses combination approaches, namely, contingency factor analysis (CFA), the cross-industry standard process for data mining (CRISP-DM) and knowledge management system development life cycle (KMSDLC). The CFA for KM solution design is performed by identifying KM processes and KMS priorities. The CRISP-DM for decision classification model is done by using a decision tree algorithm. The KMSDLC is used to develop a web-based DSS.

The highest priority requirements of KM technology for QMFFP are data mining and DSS with predictive features. The main finding of this study is to show that web-based DSS (functions and outputs) can support and accelerate QMFFP decisions by regulations and field practice needs. The DSS was developed using the CTree algorithm model, which has six main attributes and eight rules.

This study proposes a novel comprehensive framework for developing DSS (combination of CFA, CRISP-DM and KMSDLC), a novel classification model resulting from comparing two decision tree algorithms and a novel web-based DSS for QMFFP.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

This paper aims to develop a model that supports public organisations in making informed strategic decisions as to which public services are most suitable to be improved through co-creation. Thus, it first identifies the features that make public services (un)suitable for co-creation and then applies this knowledge to develop a multi-criteria decision support model for the assessment of their co-creation readiness.

The decision support model is the result of design science research. While its structure is determined by a qualitative multi-criteria decision analysis, its substance builds on a content analysis of Web of Science papers and over a dozen empirical case studies.

The model is comprised of 13 criteria clustered into two groups: service readiness criteria from the perspective of service users and service readiness criteria from the perspective of a public organisation.

The model attributes rely on a limited number of empirical cases and references from the literature review. The model was tested by only one public organisation on four of its services.

The paper shifts the research focus from organisational properties and capacity, as the key co-creation drivers and barriers, to features of public services as additional factors that affect the prospect of co-creation. Thus, it makes a pioneering step towards the conceptualisation of the idea of “service readiness for co-creation” and the development of a practical instrument that supports co-creation in the public sector.

Various approaches have emerged to assist practitioners in making more informed decisions in highway construction projects. However, industry practitioners are still using subjective ways to make decisions. Also, researchers have developed tools and techniques with similar objectives. Lack of information on what has been developed might lead to those issues. Therefore, this paper aims to review trends of evolution, pinpoint strengths and gaps in the literature and identifies potential future directions for decision-making research in highway construction projects.

A systematic review was conducted on published articles on decision-making in highway construction projects using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique.

The analysis of 101 articles revealed that existing decision-making research in highway construction projects targets improvements in four areas: feasibility, conceptual, detailed scope and detailed design. The four areas consist of sixteen subthemes that are detailed in this study. In addition, most research involved developing decision support tools and systems as well as decision-making models, techniques and frameworks. Lastly, several research areas have emerged, such as adding more decision criteria including those with uncertainties, expanding existing decision-making models into decision support systems, benchmarking decision criteria between different sample populations and exploring inter-and intra-relationships between decision criteria.

This paper provides an overview of existing research on decision-making in highway construction projects. Also, it reveals research gaps in the body of knowledge to point out directions for future research. Finally, industry practitioners can use the findings to develop strategies for effective decision-making processes.

The purpose of this study is to develop an operational level decision support system model for air traffic controllers (ATCos) within the framework of the Flexible Use of Airspace (FUA) concept to enable more efficient use of airspace capacity. This study produces a systematic solution to the route selection process so that the ATCo can determine the most efficient route with an operational decision support system model using Dijkstra’s Shortest Path Algorithm.

In this study, a new decision support system model for ATCos in decision-making positions was recommended and used. ATCos use this model as a main model for determining the shortest and safest route for aircraft as an operational-level decision support system. Dijkstra Algorithm, used in the model, is defined step by step and then explained with the pseudocode.

It has been determined that when the FUA concept and DSS are used while the ATCo chooses a route, significant fuel, time and capacity savings are achieved in flight operations. Emissions resulting from the negative environmental effects of air transportation are reduced, and significant capacity increase can be achieved. The operational level decision support system developed in the study was tested with 55 scenarios on the Ankara–Izmir flight route compared to the existing fixed route. The results for the proposed most efficient route were achieved at 11.22% distance (nm), 9.36%-time (min) savings and 837.71 kg CO₂ emission savings.

As far as the literature is reviewed, most studies aimed at increasing airspace efficiency produce solutions that try to improve rather than replace the normal process. Considering the literature positioning of this study compared to other studies, the proposed model provides a new systematic solution to the problems that cause human-induced route inefficiency within the framework of the FUA concept.

This study aims to assess the predictive performance of various factors on Bitcoin returns, used for the development of a robust forecasting support decision model using machine learning techniques, before and during the COVID-19 pandemic. More specifically, the authors investigate the impact of the investor's sentiment on forecasting the Bitcoin returns.

This method uses feature selection techniques to assess the predictive performance of the different factors on the Bitcoin returns. Subsequently, the authors developed a forecasting model for the Bitcoin returns by evaluating the accuracy of three machine learning models, namely the one-dimensional convolutional neural network (1D-CNN), the bidirectional deep learning long short-term memory (BLSTM) neural networks and the support vector machine model.

The findings shed light on the importance of the investor's sentiment in enhancing the accuracy of the return forecasts. Furthermore, the investor's sentiment, the economic policy uncertainty (EPU), gold and the financial stress index (FSI) are the top best determinants before the COVID-19 outbreak. However, there was a significant decrease in the importance of financial uncertainty (FSI and EPU) during the COVID-19 pandemic, proving that investors attach much more importance to the sentimental side than to the traditional uncertainty factors. Regarding the forecasting model accuracy, the authors found that the 1D-CNN model showed the lowest prediction error before and during the COVID-19 and outperformed the other models. Therefore, it represents the best-performing algorithm among its tested counterparts, while the BLSTM is the least accurate model.

Moreover, this study contributes to a better understanding relevant for investors and policymakers to better forecast the returns based on a forecasting model, which can be used as a decision-making support tool. Therefore, the obtained results can drive the investors to uncover potential determinants, which forecast the Bitcoin returns. It actually gives more weight to the sentiment rather than financial uncertainties factors during the pandemic crisis.

To the authors’ knowledge, this is the first study to have attempted to construct a novel crypto sentiment measure and use it to develop a Bitcoin forecasting model. In fact, the development of a robust forecasting model, using machine learning techniques, offers a practical value as a decision-making support tool for investment strategies and policy formulation.

This study aims to develop an inclusive, multidisciplinary, flexible and organizationally adaptable safety risk management framework, including diversity management, that will be implemented to ensure safety is and remains at the desired level. If the number of incidents and potential incidents that could lead to accidents and their impact rates are to be reduced operationally and administratively, aviation safety risks and sources of risk must be better understood, sources of risk identified, and the safety risk management framework designed in an organization-specific and organization-wide sustainable way. At this point, it is necessary to draw the conceptual framework well and to define the boundaries of the concepts well. In this study, a framework model that can be adapted to the organization is proposed to optimize the management of risks and provide both efficient and effective resource allocation and organizational structure design in its operations and management functions.

The qualitative research method – triple techniques – was deemed appropriate for this study, which aims to identify, examine, interpret and develop the situations of safety management models. In this context, document analysis, business process modeling technique and Delphi techniques from qualitative research methods were used via integration as the methodology of this research.

To manage dynamic civil aviation management activities and business processes effectively and efficiently, the risk management process is the building block of the “Proposed Process Model” that supports the decision-making processes of aviation organizations and managers. This “Framework Conceptual Model” building block also helps build capacity and resilience by enabling continuous development, organizational learning, and flexible structuring.

This research is limited to air transportation and aviation safety management issues. This research is limited specifically to a safety-based risk management framework for the aviation industry. This research may have social implications as source saving, optimum resource use and capacity building will make a contribution to society and add value besides operational and practical implementation.

This research may contribute to more safe operations and functions in the aviation industry.

Management and academia may gain considerable support from this research to manage their safety risks via a corporate-tailored risk management framework, both improving resilience and developing corporate capacity. With this model presented, decision-makers will have a guiding structure that can optimally manage the main risk types that may be encountered in the safety risk in the fields of suppliers, manufacturers, demand changes, logistics, information management, environmental, legal and regulatory. Existing studies in the literature are generally in the form of algorithms and cannot be used as a decision-making support tool. This model aims to fill the gap in the literature. In addition, added value may be created by applying this model to optimum management safety risks in the real aviation industry and its related sectors.

The study aims to identify and evaluate relevant criteria in the proposal and support of public administration policies for preventive maintenance comprised in a conservation approach to built heritage and aligned with local sustainable development of the historic center of the city of Sobral, in Brazil.

A novel multicriteria decision model adopting the Bayesian best-worst method is presented and its application and results are described. Though a systematic procedure, criteria were selected in order to protect the tangible and intangible values of cultural heritage, as well as its sustainable development. Then experts evaluate these criteria through an elicitation instrument.

The results show that for the decision problem over preventive maintenance, social contribution and historical record of built heritage are more important than its structural vulnerability, while architecture is less relevant. Due to the low restrictions, the subcriterion related to this property has the least influence. The weights can assist in the characterization of measures and policies for the protection of the built cultural heritage.

The use of a novel decision-making method in cultural heritage is an important initiative, given the frequent use of simple and inefficient methods. The identified and weighted criteria are important data to characterize the scenario and the topic. The results contribute to protection and development of the built heritage, encouraging the implementation of preventive conservation in the historic center, conferring to the public administration valuable information to support and propose initiatives.